Correlation between the strength of low-temperature T-linear normal-state resistivity and TcT_{\rm c} in overdoped electron-doped cuprate superconductors

The recently observed an intimate link between the nature of the strange metallic normal-state and superconductivity in the overdoped electron-doped cuprate superconductors is calling for an explanation. Here the intrinsic correlation between the strength of the low-temperature linear-in-temperature normal-state resistivity and superconducting transition temperature $T_{\rm c}$ in the overdoped electron-doped cuprate superconductors is studied within the framework of the kinetic-energy-driven superconductivity. On the one hand, the main ingredient is identified into a electron pairing mechanism involving {\it the spin excitation}, and then $T_{\rm c}$ has a dome-like shape doping dependence with the maximal $T_{\rm c}$ that occurs at around the optimal electron doping. On the other hand, in the normal-state above $T_{\rm c}$, the low-temperature linear-in-temperature normal-state resistivity in the overdoped regime arises from the momentum relaxation due to the electron umklapp scattering mediated by {\it the same spin excitation}. This {\it same spin excitation} that governs both the electron umklapp scattering responsible for the low-temperature linear-in-temperature normal-state resistivity and electron pairing responsible for superconductivity naturally generates a correlation between the strength of the low-temperature linear-in-temperature normal-state resistivity and $T_{\rm c}$ in the overdoped regime.Comment: 12 pages, 6 figures. arXiv admin note: text overlap with arXiv:2211.0308

T-linear resistivity in the strange-metal phase of cuprate superconductors due to umklapp scattering from a spin excitation

The strange-metal phase of cuprate superconductors exhibits a linear in temperature resistivity, however, the origin of this remarkable anomaly is still not well understood. Here the linear temperature dependence of the electrical resistivity in the strange-metal phase of cuprate superconductors is investigated from the underdoped to overdoped regimes. The momentum dependence of the transport scattering rate arising from the umklapp scattering between electrons by the exchange of the spin excitation is derived and employed to calculate the electrical resistivity by making use of the Boltzmann equation. It is shown that the antinodal umklapp scattering leads to the linear in temperature resistivity in the low-temperature with the temperature linear coefficient that decreases with the increase of the doping concentration, however, the nodal umklapp scattering induces a deviation from the linear in temperature resistivity in the far lower temperature, and then the quadratic in temperature resistivity in the far lower temperature is generated by both the antinodal and nodal umklapp scattering. The theory also shows that the same spin excitation that acts like a bosonic glue to hold the electron pairs together also mediates scattering of electrons in the strange-metal phase of cuprtae superconductors responsible for the linear in temperature resistivity and the associated electronic structure.Comment: 16 pages, 11 figure

Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis.

BACKGROUND: This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database. METHODS: Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram. RESULTS: For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P \u3c 0.001) and 0.854 (95% CI 0.785-0.924, P \u3c 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P \u3c 0.001) and 0.809 (95% CI 0.680-0.939, P \u3c 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P \u3c 0.0001). CONCLUSIONS: Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis

Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery

With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.Comment: Accepted by TGR

Effects of fully open-air [CO2] elevation on leaf photosynthesis and ultrastructure of Isatis indigotica Fort

Traditional Chinese medicine relies heavily on herbs, yet there is no information on how these herb plants would respond to climate change. In order to gain insight into such response, we studied the effect of elevated [CO2] on Isatis indigotica Fort, one of the most popular Chinese herb plants. The changes in leaf photosynthesis,chlorophyll fluorescence, leaf ultrastructure and biomass yield in response to elevated [CO2] (550619 mmol mol–1) were determined at the Free-Air Carbon dioxide Enrichment (FACE) experimental facility in North China. Photosynthetic ability of I. indigotica was improved under elevated [CO2]. Elevated [CO2] increased net photosynthetic rate (PN), water use efficiency (WUE) and maximum rate of electron transport (Jmax) of upper most fully-expended leaves, but not stomatal conductance (gs), transpiration ratio (Tr) and maximum velocity of carboxylation (Vc,max). Elevated [CO2] significantly increased leaf intrinsic efficiency of PSII (Fv’/Fm’) and quantum yield of PSII(WPSII), but decreased leaf non-photochemical quenching (NPQ), and did not affect leaf proportion of open PSII reaction centers (qP) and maximum quantum efficiency of PSII (Fv/Fm). The structural chloroplast membrane, grana layer and stroma thylakoid membranes were intact under elevated [CO2], though more starch grains were accumulated within the chloroplasts than that of under ambient [CO2]. While the yield of I. indigotica was higher due to the improved photosynthesis under elevated [CO2], the content of adenosine, one of the functional ingredients in indigowoad root was not affected

Introducing Depth into Transformer-based 3D Object Detection

In this paper, we present DAT, a Depth-Aware Transformer framework designed for camera-based 3D detection. Our model is based on observing two major issues in existing methods: large depth translation errors and duplicate predictions along depth axes. To mitigate these issues, we propose two key solutions within DAT. To address the first issue, we introduce a Depth-Aware Spatial Cross-Attention (DA-SCA) module that incorporates depth information into spatial cross-attention when lifting image features to 3D space. To address the second issue, we introduce an auxiliary learning task called Depth-aware Negative Suppression loss. First, based on their reference points, we organize features as a Bird's-Eye-View (BEV) feature map. Then, we sample positive and negative features along each object ray that connects an object and a camera and train the model to distinguish between them. The proposed DA-SCA and DNS methods effectively alleviate these two problems. We show that DAT is a versatile method that enhances the performance of all three popular models, BEVFormer, DETR3D, and PETR. Our evaluation on BEVFormer demonstrates that DAT achieves a significant improvement of +2.8 NDS on nuScenes val under the same settings. Moreover, when using pre-trained VoVNet-99 as the backbone, DAT achieves strong results of 60.0 NDS and 51.5 mAP on nuScenes test. Our code will be soon.Comment: revisio

Multimodal critical-scenarios search method for test of autonomous vehicles

Purpose – The purpose of this paper is to search for the critical-scenarios of autonomous vehicles (AVs) quickly and comprehensively, which is essential for verification and validation (V&V). Design/methodology/approach – The author adopted the index F1 to quantitative critical-scenarios' coverage of the search space and proposed the improved particle swarm optimization (IPSO) to enhance exploration ability for higher coverage. Compared with the particle swarm optimization (PSO), there were three improvements. In the initial phase, the Latin hypercube sampling method was introduced for a uniform distribution of particles. In the iteration phase, the neighborhood operator was adapted to explore more modals with the particles divided into groups. In the convergence phase, the convergence judgment and restart strategy were used to explore the search space by avoiding local convergence. Compared with the Monte Carlo method (MC) and PSO, experiments on the artificial function and critical-scenarios search were carried out to verify the efficiency and the application effect of the method. Findings – Results show that IPSO can search for multimodal critical-scenarios comprehensively, with a stricter threshold and fewer samples in the experiment on critical-scenario search, the coverage of IPSO is 14% higher than PSO and 40% higher than MC. Originality/value – The critical-scenarios' coverage of the search space is firstly quantified by the index F1, and the proposed method has higher search efficiency and coverage for the critical-scenarios search of AVs, which shows application potential for V&V